Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision
This addresses the challenge of expensive annotation requirements in biomedical domains by enabling joint entity and relation extraction without mention-level supervision, though it is incremental as it builds on existing tasks.
The paper tackles the problem of entity linking and relation extraction in biomedical text without needing mention-level supervision, which is typically expensive and unavailable. The proposed model simultaneously performs both tasks, outperforming a state-of-the-art pipeline on two biomedical datasets and significantly improving overall recall.
Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision -- which is not available in many domains. Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. This approach avoids cascading errors that arise from pipelined methods and more accurately predicts entity relationships from text. We show that our model outperforms a state-of-the art entity linking and relation extraction pipeline on two biomedical datasets and can drastically improve the overall recall of the system.